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Distribution and movement of a stocked freshwater fish:implications of a variable habitat volume for stockingprograms

James A. SmithA,B,E, Lee J. BaumgartnerC, Iain M. SuthersA,B

and Matthew D. TaylorA,B,D

AEvolution and Ecology Research Centre and School of Biological, Earth and Environmental

Science, University of New South Wales, Sydney, NSW 2052, Australia.BSydney Institute of Marine Science, Building 22, Chowder Bay Road, Mosman,

NSW 2088, Australia.CFisheries and Ecosystems Research, Narrandera Fisheries Centre, NSW Department

of Primary Industries, Buckingbong Road, Narrandera, NSW 2700, Australia.DCronulla Fisheries Research Centre, NSW Department of Primary Industries,

202 Nicholson Parade, Cronulla, NSW 2230, Australia.ECorresponding author. Email: [email protected]

Abstract. Fish are commonly stocked into impoundments globally, yet their patterns of habitat use in this variableenvironment are rarely incorporated into the management of stocking density. The movement and distribution of

Australian bass Macquaria novemaculata (Perchichthyidae) were monitored in two impoundments to assess whether:(1) impoundment populations exhibit behaviour typical of wild or riverine percichthyids; (2) changing gradients oftemperature and dissolved oxygen influenced distribution; and (3) the volume of available habitat should be incorporatedinto the management of these fisheries. Habitat use was determined with a combination of gill netting and ultrasonic

telemetry using depth-coded tags. Tagged fish displayed both crepuscular and migratory behaviour typical of thePercichthyidae, but also showed a previously unobserved division between littoral and pelagic foraging strategies.Australian bass showed no obvious thermal preferences, but avoided areas with dissolved oxygen ,4mgL�1. In one

impoundment, a combination of hypoxia andwater extraction reduced the volume of available habitat to 15%ofmaximumin March 2009, which coincided with increased catch per unit effort (CPUE) and decreased fish condition. The adaptivebehaviour of Australian bass makes them well suited to the variability of impoundments, but annual and stochastic events

of habitat reduction should be considered when planning stocking regimes for these fisheries.

Additional keywords: acoustic tags, CPUE, extraction, Fulton’s K, habitat availability, hypoxia, Percichthyidae,tracking.

Received 31 May 2011, accepted 3 September 2011, published online 12 October 2011

Introduction

Identifying the distribution of fishes in relation to environmentalvariation is an important component of fisheries management

(Hayes et al. 1996; Rice 2005). For fisheries that are enhanced ormaintained through stocking, this knowledge is essential fordeveloping responsible stocking strategies (Cowx 1994; Taylor

et al. 2005). Of relevance to stocked fisheries is how muchhabitat of sufficient quality is actually available to stocked fishand how this varies temporally. This is observed in modelsthat include a spatial component to estimate a carrying capacity

(Luo et al. 2001) or determine an optimal stocking density(Taylor and Suthers 2008). In particular, a variable habitatvolume is potentially an important factor affecting the success

of stocking programs and warrants further investigation. The

movement, habitat use and home range of fishes is also impor-tant information for themanagement of stocked or wild fisheriesand can be used to identify refuge areas (Taylor et al. 2006;

Taylor and Ko 2011) and foraging behaviours (Kobler et al.2009). However, this information is unknown for many stockedspecies.

Identifying the environmental variables associated with thelimits of a species’ distribution is essential when explainingbroad distribution patterns (Swales 2006) and for predicting thevolume of available habitat in stocked fisheries. Interactions

between habitat quality and species-specific preferences candrive the fine-scale distribution of fish in their natural environ-ment (Hayes et al. 1996; Rosenfeld 2003). However, on a

broader scale, the abiotic requirements and physiological

CSIRO PUBLISHING

Marine and Freshwater Research, 2011, 62, 1342–1353

http://dx.doi.org/10.1071/MF11120

Journal compilation � CSIRO 2011 www.publish.csiro.au/journals/mfr

tolerances of a species can determine the amount of habitat thatis actually available. Two abiotic factors that influence habitat

availability in closed waters are dissolved oxygen (DO) andtemperature (Coutant 1985; Dillon et al. 2003). Closed waterssuch as impoundments are frequently subject to thermal stratifi-

cation in the warmermonths, which can create a hypoxic bottomlayer under a warm surface layer (Summerfelt 1999). If thethermal and DO conditions exceed a species’ tolerance levels,

then these areas become unavailable to fishes (Coutant 1985;Roberts et al. 2009), which can increase fish density in remain-ing habitats. Hypoxia is a particularly effective determinant ofthe vertical distribution of fishes because physiological limits

determine the lower DO threshold for a species (Pollock et al.

2007). Environmental thresholds have been closely examinedfor many species but their importance in influencing the habitat

availability of stocked fisheries remains largely unexplored. Inaddition to these abiotic stressors, variations in flow regimeand impoundment releases can result in large and rapid fluctua-

tions in the water level of closed waters. These fluctuationscan be a key driver of impoundment ecology (Welcommeet al. 2010), yet the potential impacts on stocked or wild fishpopulations have rarely been examined, with the exception

of spawning success in the littoral zone (Zalewski et al. 1995;Kahl et al. 2008).

The impoundments of eastern Australia are often stocked

with nativemembers of the family Percichthyidae, including theAustralian bass Macquaria novemaculeata, yet the distributionand movement of percichthyids is known mostly from riverine

studies (but see Ebner et al. 2010; Ebner et al. 2011). Despite theobvious value of describing the determinants of habitat avail-ability for the management of stocked fish, an analysis of the

minimum habitat requirements for a stocked percichthyid islacking. Ultrasonic transmitters capable of transmitting depthinformation, which have been used to assess the association ofindividual fish with the thermocline (Nowak and Quinn 2002),

were used in the current study to determine the availability ofhabitat for stocked fish. Depth tags also improve the resolutionof individual foraging strategies by enabling the identification of

associations between tagged fish and the benthic and surfacezones in addition to the pelagic and littoral zones.

The objectives of this study were to quantify the volume of

habitat used by Australian bass and to examine the implicationsof thermal stratification, hypoxia and variation in impound-ment volume on habitat availability. Specifically, we aimedto address whether: (1) the home range and movement

patterns of Australian bass in impoundments are typical ofthe Percichthyidae; (2) the stratification of impoundmentsinfluence the volume of available habitat for Australian bass,

based on their specific requirements of temperature and DO;and (3) variation in an impoundment’s available habitat volumecan have a measurable impact on the density and condition of a

stocked fish.

Materials and methods

Species and study areas

Australian bass (Macquaria novemaculeata) is an Australian

percichthyid and a popular native sport fish. Impoundmentpopulations of this species form important recreational fisheries

that are maintained solely through stocking, because Australianbass is catadromous and cannot reproduce in fresh water (Harris

1986). Approximately 300 000–500 000 Australian bass arereleased to maintain impoundment fisheries in eastern Australiaeach year (C. Westaway, personal communication).

This study was conducted in Danjera Dam (34.9208S,150.3858E) and Brogo Dam (36.4918S, 149.7408E), in NSW,Australia (Fig. 1). The telemetry component of the study was

undertaken in Danjera Dam, which is a storage for residentialwater supply (7800ML capacity, 0.9 km2 surface area) and has amaximum depth of 30m. Gill netting and electric fishing wereundertaken in Brogo Dam, which is a storage for mainly

agricultural water supply (9000ML capacity, 1 km2 surfacearea) and has a maximum depth of 25m. Both impoundmentsare stocked intermittently with Australian bass to maintain

recreational fisheries and other than short-finned eels (Anguillaaustralis), there are no other large predatory fish found in eitherdam. Brogo Dam can experience high levels of water extraction

and had large fluctuations in total volume during this study(20–100% capacity).

Telemetry

Telemetry was used in Danjera Dam to estimate the homerange and movement patterns of Australian bass and to record

their association with the stratified zone. Eleven Australianbass (135–696 g) were fitted with ultrasonic transmitters inSeptember 2009. Fish were captured at one of two sites

(Fig. 1) using gill netting (,30min soak time) and monitoredfor 30min before tagging. Sonotronics IBT tags (SonotronicsInc., Tuscon, AZ, USA) were implanted in fish that displayedno obvious adverse effects of capture. Three different tag sizes

were used to enable the tagging of a range of fish sizes and tomeet the temporal and spatial requirements of the study. Threesmall fish (,230 g) were tagged externally with small trans-

mitters with a 3 week battery life (model IBT-96-1-I, Table 1).These transmitters were attached using t-bar tags (Bradfordet al. 2007) with the transmitter bound and glued to the trailer.

Five fish were tagged externally using the same methodwith transmitters that also transmit depth information with a10 day battery life (model IBDT-97-1-I, Table 1), enablingthe resolution of vertical movement. Three large fish were

tagged with transmitters with a 5 month battery life (modelIBT-96-5-I, Table 1). These larger transmitters enabled thetracking of individuals over a period of months and were

implanted surgically. Fish were anaesthetised using Aqui-S60mgL�1(Barker et al. 2002) and surgery was performedaccording to the method by Taylor et al. (2006). Surgery took

,5min, at which point the fish was returned to a tank con-taining fresh dam water. All fish visibly recovered within10min and were observed in captivity for 1 h before they were

released at the point of capture.Each transmitter had a unique pulse rate/frequency combi-

nation to allow identification of individual fish and the IBDTtransmitters had an additional pulse string, which transmitted

their depth by varying the pulse interval time. Fish weremanually tracked using a boat-based acoustic receiver (Sono-tronics USR-96; Sonotronics Inc.) and directional hydrophone

(Sonotronics DH-4) mounted facing-forward on the starboardbow and signal codes interpreted aurally through headphones.

Movement of Australian bass in impoundments Marine and Freshwater Research 1343

The method used for manually locating fish was the same as in

Taylor et al. (2006). The described technique had a horizontalaccuracy of 4.8m. Initially, fish were located every 3 h for6 days after release. In addition, the three fish tagged with

5 month tags also had their locations recorded once a month for

the following 2 months. Tracking was restricted to between theDanjera Dam wall and,2.3 km upstream (Fig. 1). When IBDTtags were detected, the duration of the pulse string indicating

Dam wall

Depth (m)

0

27

0 250 500

Metres

New South Wales

Danjera Dam

10000 500

Metres

Brogo Dam

Danjera Dam

Brogo Dam

36.4

90�S

149.735�E 150.385�E

34.9

25�S

150.375�E

34.9

30�S

149.725�E

36.4

80�S

SydneyN

N

Fig. 1. Maps of the two study sites and the bathymetry of Danjera Dam, NSW, Australia. The dotted line represents the upper limit to the tracking area in

Danjera Dam and the white circles represent the two capture and release points. In Danjera Dam, the depth ranges from,0m (light grey) to 27m (dark grey).

Table 1. Biometric and movement information for tagged Australian bass at time of tagging

Movement values are mean (s.e.); MAI, minimum activity index (m h�1)

Fish identity

number

Total length Mass Movement (s.e.) MAI (s.e.) Core (50%)

home range

Total (95%)

home range

50 : 95

Ratio

Home range

volume

(mm) (g) (m) (m h�1) (m2) (m2) (m3)

1B 320 466 800.3 (155.3) 39.1 (5.8) 20 923 80 784 0.259

2A 267 231 Fate unknown

3B 335 563 519.4 (113.4) 37.3 (9.1) 8857 43 242 0.205

4C 348 696 Moved out of tracking area on day 2

5B 349 633 712.0 (105.8) 39.3 (4.8) 28 378 99 828 0.284

6C 260 240 385.1 (55.9) 23.3 (6.6) 12 537 50 418 0.249 291 080

7C 350 650 394.8 (67.3) 16.1 (3.0) 7427 45 506 0.163 231 171

8C 231 170 773.8 (88.9) 37.7 (4.2) 21 708 66 583 0.326 769 255

9A 224 150 1062.9 (40.5) 43.7 (14.1) Moved out of tracking area on day 3

10C 240 192 1031.7 (149.4) 44.3 (6.1) 31 198 101 059 0.309 1 158 810

11A 214 135 Fate unknown

Average 285 375 710.0 35.1 18 718 69 631 0.256 612 579

AFish tagged with 3 week tags.BFish tagged with 5 month tags.CFish tagged with depth tags.

1344 Marine and Freshwater Research J. A. Smith et al.

depth was recorded three times using a stopwatch and theaverage used to calculate the interval duration. A relationship

between depth (D) and interval duration (I) is provided bySonotronics Inc., but a regression was created independentlyfor the method used in this study, by suspending an IBDT tag at

known depths (D¼ 194.6I� 105.5, n¼ 12, R2¼ 0.995). Theaccuracy of depth measurements using this regression was1.1� 0.6m (mean� s.d.), with 90% of the estimates within

2.1m of the true depth. Vertical temperature and dissolvedoxygen profiles were constructed every ,12 h to enable theassociation of these parameters with fish depth.

Fish sampling

To assess the temperature and DO requirements of Australianbass, gill netting at discrete depths (Kahl and Radke 2006) was

conducted in Brogo Dam in May, August and December 2008and in March 2009. The depths of fish were compared withwater quality profiles, made using a hand-held water qualitymeter (Hydrolab Datasonde 4, Hach Hydromet, Loveland, CO,

USA). Horizontal multi-mesh gill nets (15m and 30m length,2m drop) were either sunk to sample the 2m above the benthosat various depths, or floated to sample the surface 2m. Themesh

sizes ranged from 25 to 100mm and regularly caught fish.100mm. The depths of submerged nets weremeasured using asounder and were recorded every 5m along the net. Nets were

submerged for between 60 and 90min. When a fish was caught,it was weighed (�1 g) and measured (�1mm) and its point ofcapture recorded as a distance along the gill net and a heightwithin the gill net, which were used to estimate the 2m depth

range at which the fish was caught. This method is unsuitablefor mid-depth areas more than 2m away from the bottom orsurface. Catch per unit effort (CPUE) was calculated as the

number of fish caught per 10 square metres of net per hour (fish10m�2 h�1). In every sampling period, sites were haphazardlyselected and sampling effort was spread equally across all

depths so that total CPUE estimates were representative of fishpresence throughout the entire dam.

To sample additional fish for the estimation of fish condition,

electric fishing was used in Brogo Dam during May andDecember 2008, March 2009 and May 2010. Electric fishingwas done using a 5m aluminium boat fitted with a Smith-Root7.5 GPP unit (Smith-Root, Vancouver, WA, USA), operating at

600–800V DC, 5A, 25% duty cycle and 120 pulses s�1.Approximately 4400 s electric fishing and 1000m2 h�1 gillnetting were conducted in each sampling period and an effort

was made to sample with equal effort during the day and aftersunset.

Analysis

Home range was calculated by fixed kernel density estimationusing the extension Hawth’s Analysis Tools ver. 3.27 (Beyer2007), which estimates a probability density function based on a

finite number of detections. The home range (m2) was defined asthe area within a contour incorporating 95% of the kernel den-sity and the core area of the home range as that within the 50%

contour (Taylor et al. 2006). No suitable method was found inthe literature to calculate a three-dimensional home range (m3),so, for each of the four fish with sufficient depth data, an

estimate was calculated by assuming the home range volumeapproximates an oblate spheroid:

V ¼ 2

3pr2d; ð1Þ

where V is the estimated volume of the total home range (m3),d is the observed depth range and r is calculated:

r ¼ A

p

� �12

; ð2Þ

where A is the area specified by the two-dimensional 95%

kernel density contour. An oblate spheroid was chosenbecause the extremities of the 95% kernel density contour aredefined by fewer detections, meaning a shape such as a

cylinder would overestimate the volume at the edges of thehome range.

Movement patterns were quantified using the minimum

activity index (MAI, m h�1) and through the assessment of dielactivity. The MAI was calculated by dividing the distancebetween detections by the time elapsed during the detections,thus representing a minimum estimate of activity (Taylor and

Ko 2011). Distances between waypoints were calculated usingArcGIS ver. 9.3.1 (ESRI, Redlands, CA, USA). If this distanceintersected the edge of the impoundment, the distance was

recalculated as the minimum non-intersecting distance. Dielactivity patterns were examined using a proportional distancevalue, which partitioned the total distance moved during the

experiment among eight daily tracking periods. Proportionswere used for this analysis because they removed any inherentdifferences in overall activity between individuals. To reduce

the bias of unknown movement, only MAI and proportionaldistance values calculated from consecutive detections wereanalysed. ANOVA was used to test for differences in theproportional distance values between the daily tracking periods

and Tukey’s HSD test was used to examine differences betweenspecific periods. The data were not transformed, as the inspec-tion of box-plots and normal probability plots showed sufficient

homoscedasticity and normality of the data.The association of Australian bass with specific habitat

zones, including the stratified zone, was assessed using the

relative-depth plot. Tracking data can be used in these plots,assuming the bathymetry and the depth of the fish is known. Therelative-depth plot can identify associations not only the littoral

and pelagic zones, which are often used to define variation inhabitat use (Biro et al. 2003b; Kobler et al. 2009), but also thesurface and epibenthic zones, which are not distinguished usingtwo-dimensional tracking data. Bathymetric information for use

in these plots was created by interpolating ,800 global posi-tioning system (GPS) waypoints and associated depth readingsthroughout the Danjera Dam tracking area (ArcGIS ver. 9.3.1).

To examine the impact that variations in habitat volumemight have on Australian bass, gill netting CPUE and fishcondition were compared with the volume of available habitat

in Brogo Dam for each sampling period. CPUE was calculatedas an average of all individual gill net deployments within eachsampling period. Available habitat was calculated as the volumebetween the surface and the depth at which dissolved oxygen


dropped to 4mgL�1 and is given as a percentage of maximumimpoundment volume. This volume was calculated using a

volume–depth profile (Plumb and Blanchfield 2009) and theimpoundment volume at each sampling period, both of whichwere obtained from the NSW Office of Water (personal com-

munication). Fish condition was calculated using a modifiedFulton’s K, discussed by Cone (1989):

K ¼ W

Lb� 10n; ð3Þ

whereK is the index of fish condition,W is themass of a fish, L isits length, n is a scaling integer (n¼ 100) and b is the exponent of

the length-weight least-squares regression:

W ¼ aLb; ð4Þ

where W and L are as above and a is a constant. The regression

was calculated using Australian bass caught in all five samplingperiods by gill netting and electric fishing and the resultant bwas used to calculate fish condition for all five sampling

periods (b¼ 2.995, R2¼ 0.995, n¼ 764). For analysis, 92 fish(the minimum number caught in a sampling period) wereselected at random from each period. Fish weighing less than

five grams were excluded from the length–weight regressionand condition calculations due to considerable uncertainty intheir masses (�1 g). ANOVA and Tukey’s HSD test were usedon untransformed data to examine the differences in fish con-

dition between sampling periods. CPUE was not analysed, asno transformation was found to meet the assumptions of aparametric analysis and non-parametric tests were deemed

unsuitable. All statistical tests were performed in JMP (7.0, SASInstitute, Cary, NC, USA).

Results

Home range and movement patterns

Australian bass moved, on average, a minimum of 700m day�1,at an average speed of ,35m h�1 in Danjera Dam (Fig. 1;

Table 1). After the six tracking days, fish had an average corehome range of 18 700m2 and an average total home range of69 600m2, or,2 and 8% of the dam’s surface area respectively.

The core range was,25% the size of the total home range. Thetotal range volume for those fish with depth tags was an averageof 612 579m3 (612.6ML), or ,8% of the dam’s capacity. Thecore range was variably made up of 1–3 separate areas (Fig. 2),

to which fish showed varying degrees of fidelity. Fish 1, 3 and 5,which were all fitted with longer duration tags, were observedwithin their same core ranges in the 2 months following the

tracking study. Fish movement was elevated during dawn(around 0600 hours) and to a lesser extent dusk (1800 hours),with proportionally greater movement than other periods of the

day (ANOVA, F7,47¼ 5.04, P, 0.001; see Fig. 3 for results ofTukey’s HSD test). The midday period showed the least amountof proportional movement (Fig. 3).

Not all tagged fish were continually located during the entirestudy (Table 1). Two fish disappeared from the main trackingarea after 12 h and only two detections each (fish 2 and 11). Bothwere implanted with the smaller tags (IBT-96–1-I tags) and it is

possible the tags failed or that the fish exited the tracking areawithin a 3-h period. Fish 4 and 9 were suspected of leaving thetracking area on the second and third days respectively (Table 1),

Fish 1

Fish 10Fish 8Fish 7

Fish 6Fish 5Fish 3

0 250 500125

Metres

N

Fig. 2. Home ranges of individualAustralian bass inDanjeraDam,NSW,Australia. The dark grey area represents

the core range (50% kernel density) and the dark and light grey area represents the total range (95% kernel density).

Values correspond with those presented in Table 1.


as the final detections of these fish were near the upstreamboundary of the tracking area. There were insufficient detec-

tions to calculate a home range for fish 4 or 9, or movementindices for fish 4.

Relative-depth plots (of fish 6, 7, 8, 10) show individualvariation in habitat zone association (Fig. 4). Fish 8 and 10 were

either pelagic or benthic. These fish moved into deeper sectionsduring the day but avoided the surface. Fish 6 and 7 preferredlittoral and surface zones and only moved into the pelagic zone

at night.

Abiotic variability and habitat availability

Fish were caught from all sampled depths in Brogo Dam when

no stratification was present (August 2008) (Fig. 5). The verticaldistribution of Australian bass was more restricted duringperiods with stratification and no fish were collected belowthe thermocline where DO was less than 4mgL�1. Acoustic

telemetry in Danjera Dam showed that some fish briefly toler-ated DO of 2–4mgL�1(Fig. 4). Australian bass were associatedwith a range of different temperatures (9–218C), including a

range of 10–208C within the same sampling period (December2008) (Fig. 5).

The percentage volume of available habitat in Brogo Dam

varied substantially due to stratification and a fluctuatingimpoundment volume. The impoundment volume for eachsampling period, in chronological order, was 97, 100, 62 and

25% (Fig. 6). The volume had returned to 100%capacity byMay2010. Average CPUE from gill netting did not show a clearrelationship with habitat volume, but did increase more than4-fold in March 2009, when the volume of available habitat

reduced to 15% (Fig. 6). Fish condition decreased substantially

Fish 8

Bottom depth at location

10 15 20 25

Fis

h de

pth

at lo

catio

n

25

20

15

10

5

0

Fish 7


10 15 20 25

Fis

h de

pth

at lo

catio

n

25

20

15

10

5

0Fish 6


10 15 20 25

Fis

h de

pth

at lo

catio

n

25

20

15

10

5

0

Fish 10


0 5

0 50 5

0 5 10 15 20 25

Fis

h de

pth

at lo

catio

n

25

20

15

10

5

0

Littoral Surface

Epibenthic

Pelagic

Fig. 4. Relative-depth plots, indicating fish depth and bottom depth during the day (white) and night (black), for the four fish with

sufficient telemetry data. The dashed line represents the depth at which dissolved oxygen equals 4mgL�1 and the solid line

2mgL�1. The different habitat zones represented by different areas of the plot are given.

Sampling time

12 15 18 21

Pro

port

ion

of to

tal d

ista

nce

mov

ed

0.0

0.1

0.2

0.3A

AB

B B B

B

B

B

0 3 6 9

Fig. 3. Mean movement (þs.e.) of individuals expressed as the proportion

of distance travelled over a 24-h period. Sampling time is the approximate

time detections were made (‘0’ is midnight). Data include distances

calculated using only consecutive detections. The results of Tukey’s HSD

test are given (times not sharing a letter are significantly different).


at this time (Fig. 7) and was significantly lower than othersampling periods except for August 2008 (ANOVA,

F4,455¼ 11.95, P, 0.001; Fig. 7).

Discussion

Home range and movement patterns

Impoundment populations of stocked Australian bass had home

ranges andmovement patterns typical of the Percichthyidae, buttrackingwith depth tags revealed traits previously unobserved inthis family. These behavioural differences aremost likely drivenby unique features of the impoundment environment, rather than

an inherent difference between stocked and wild fish. The totalhome range for Australian bass was similar in size to thatobserved for wild Macquarie perch (Percichthyidae) in a res-

ervoir, tracked for approximately the same duration (Ebner et al.2011). These home ranges in impoundments appear larger thanthose of a similar species in riverine environments (Thiem et al.

2008; Ebner and Thiem 2009). This may be the result of con-sistently larger home ranges in impoundment environments(Minns 1995), or due to difficulties calculating home rangeareas in convoluted riverine systems (Ebner et al. 2010). The

distance moved per day, however, seems to be similar betweenthe Australian bass in the current study and some riverinepercichthyids (Simpson and Mapleston 2002; Koehn et al.

2009). Australian bass were also observed to have multiple core

areas within a home range, in a similar manner to those observedin impoundment populations of wild Macquarie perch (Ebner

et al. 2011). The total range for Australian bass was 8% of theimpoundment, both in surface area and volume, which suggestsAustralian bass are consistent users of both the horizontal and

vertical habitat.The ratio of core range to total range in the current study

(25.6%) is similar to the ratio observed forwildMacquarie perch

in an impoundment (20.8%; Ebner et al. 2011), which is almostcertainly due to crepuscular foraging in these species. Crepus-cular movements of Australian bass were strongly observed,with peaks in activity occurring at dawn and, to a lesser extent,

dusk. Crepuscular predation was a suspected trait of Australianbass (Harris 1985), but from mostly anecdotal evidence. Dawnand dusk as periods for peaks in activity is often observed in

other fish species (Ng et al. 2007), including some percichthyids(Thiem et al. 2008; Ebner et al. 2009a, 2009b). Crepuscularforaging is a common strategy for species that prey on vertical-

ly-migrating zooplankton or zooplanktivorous fish (Reebs2002) and Australian bass are known to consume both preytypes (Harris 1985; Smith et al. 2011).

Migratory diel movements were observed in Australian bass,

which is a novel finding for this species and is probably the resultof predatory behaviour by Australian bass. The relative-depthplots show the two pelagic/epibenthic fish moving up in the

water column at night and the two littoral fish moving out from

August 2008

Depth (m)

CP

UE

(#

fish

10 m

�2 h

�1 )

0

1

2

3

Dis

solv

ed o

xyge

n (m

g L�

1 )

0

5

10

15

Tem

pera

ture

(�C

) 8

10

12

14

16

18May 2008

Depth (m)

10 12 14 16 18 20

CP

UE

(#

fish

10 m

�2 h

�1 )

0

1

2

3

Dis

solv

ed o

xyge

n (m

g L�

1 )

0

5

10

15

Tem

pera

ture

(�C

)

8

10

12

14

16

18

December 2008

Depth (m)

10 12 14 16 18 200

1

2

3

Dis

solv

ed o

xyge

n (m

g L�

1 )

0

5

10

15

8

10

12

14

16

18

20

22March 2009

0

2

4

6

8

0

5

10

15

8

10

12

14

16

18

20

22

0 2 4 6 8 10 12 14 16 18 200 2 4 6 8

Tem

pera

ture

(�C

)

0 2 4 6 8

CP

UE

(#

fish

10 m

�2 h

�1 )

Depth (m)

10 12 14 16 18 200 2 4 6 8

Dis

solv

ed o

xyge

n (m

g L�

1 )

Tem

pera

ture

(�C

)

CP

UE

(#

fish

10 m

�2 h

�1 )

Fig. 5. Vertical distribution of Australian bass in BrogoDam, NSW,Australia, observedwith gill netting, expressed as the total number of fish caught in each

depth bin in each sampling period, standardised to effort in that depth bin (catch per unit effort, CPUE).


the littoral zone at night. These movements coincide with themovement of migratory prey. Zooplankton often migrate verti-

cally on a daily basis, moving up into the shallow pelagic zone atnight (Lampert 1993), which can drive a similar migration intheir teleost predators, both vertically (Lieschke andCloss 1999)

and horizontally (Wurtsbaugh and Li 1985). This indicatesthat both the crepuscular foraging and pelagic migrations inAustralian bass could be driven by the behaviour of their prey.Although crepuscular foraging is also observed in riverine

percichthyids (e.g. Ebner et al. 2009b), the diel migrations byAustralian bassmay bemore likely in impoundments because ofthe importance of zooplankton in these environments. A previ-

ous study found that riverine Murray cod generally avoided thepelagic zone (Koehn 2009), which supports this assertion.

Significant individual variation in the use of the available

spatial niche was observed from depth tag data. Two fish wereconsistently found in the littoral/surface zones and two were

found consistently in the pelagic/epibenthic zones, with fewexcursions out of these areas. Such separation between littoral

and pelagic foraging strategies may be particularly indicative ofstable intra-population niche partitioning, as these two zones arecan show distinct food chains (Meredith et al. 2003; Quevedo

et al. 2009). The tagging of more individuals is needed toquantify the extent of spatial niche partitioning in Australianbass, but there is strong evidence that consistent separation of

the spatial niche can occur between competing species (Wernerand Hall 1979; Kahl and Radke 2006) and between individualsof the same species (Armstrong et al. 1999; Keeley 2001; Laurelet al. 2007). This possible association of Australian bass with

specific foraging zones supports the idea that the speciesexhibits strong inter-individual variation in foraging behaviour(Smith et al. 2011).

Two Australian bass were suspected of exiting the trackingarea during the study. Such movements can indicate the searchfor new home ranges (Ebner and Thiem 2009) and a consistent

proportion of ‘nomadic’ individuals in a population has beenobserved in numerous percichthyid species (Simpson andMapleston 2002; Crook 2004; O’Connor et al. 2005; Koehnet al. 2009). The presence of non-settled individuals is consid-

ered an important mechanism for dealing with temporallyor spatially variable resources (Switzer 1993; Crook 2004) orheterogeneous habitat quality (Switzer 1993). Occasional

migration appears to be a consistent trait of the Percichthyidaein both impoundment and riverine ecosystems.

The post-operative recovery time in this study was minimal

(1 h), which may have influenced the behaviour of tagged fish.The similar patterns of movement between this study and otherpercichthyid studies with longer recovery times (e.g. Ebner et al.

2009b, 2011) do suggest, however, that tagging did not havea large effect on behaviour. Australian bass tagged with t-barscan show quick recovery and little inflammation after 1 week,although fish tagged internally can show greater signs of

inflammation and eventual tag rejection (Butler et al. 2009).The time taken for the resumption of foraging behaviour posttagging is uncertain, but the percichthyid Macquarie perch has

been shown to resume normal feeding behaviour 2 days afterimplantation of an internal tag (Broadhurst et al. 2009). Track-ing studies examining the influence of post-tagging recovery

time on fish behaviour are needed if the results of studies such asthis one are to be correctly interpreted.

Abiotic variability and habitat availability

Stratification restricted the volume of habitat available forAustralian bass in this study, but it was driven by the require-ments for DO rather than temperature. Australian bass were

observed in a wide range of temperatures within the samesampling period (e.g. 10–208C in December 2008), which sug-gests that the boundaries for thermal tolerance lie outside this

range. Other species of fish in closed waters show thermalboundaries outside this range, such as striped bass (258C,Coutant 1985). The correlation between DO and temperature

can make interpretation of thermal boundaries difficultand, without observing Australian bass in a wider range oftemperatures, we cannot identify the boundaries of thermaltolerance. The data do indicate, however, that Australian bass

tolerate most temperatures likely to occur in their temperate

May 08 Aug 08 Dec 08 Mar 09

Cat

ch p

er u

nit e

ffort

0

1

2

3

Vol

ume

(%)

0

20

40

60

80

100

Fig. 6. Average catch per unit effort (CPUE) (þs.e.) using gill netting (fish

per 10m2 h�1). The white bars represent the actual CPUE and the grey bars

represent the CPUE standardised to the volume of available habitat (i.e. the

likely CPUE given 100% available habitat). The volume of available habitat

(line) and impoundment volume (dashed line) as a percentage of maximum

impoundment volume are given.

May 08 Dec 08 Mar 09 May 100.010

0.012

0.014

0.016

0.018

0.020

Fis

h co

nditi

on

Aug 08

BBC

AB

C

A

Fig. 7. Boxplots of fish condition in Brogo Dam, NSW, Australia, for the

four sampling periods. The results of Tukey’s HSD test are given (periods

not sharing a letter are significantly different).


Australian impoundments, which means that bottom hypoxia isof greater concern in these environments.

Both impoundments in this study experienced bottomhypoxia and in BrogoDam, changes in the depth of stratificationwere associated with changes in the vertical distribution of

Australian bass. The minimum DO required for fish survivalis often around 2–3mgL�1 (Coutant 1985; Suthers andGee 1986; Swales 2006), but fish behaviour may be affected

as DO drops below 5–6mgL�1(Dillon et al. 2003; Plumb andBlanchfield 2009). For Australian bass, hypoxia appears to existwhen DO drops below 4mgL�1, as fish in this study were notcaught below this concentration using depth-specific gill net-

ting. The Australian bass fitted with acoustic depth tags,however, showed that some individuals did access habitat withDO ,4mgL�1, if only occasionally. Other species have been

observed to enter the hypoxic zone briefly to a search for prey(Swales 2006; Roberts et al. 2009), including at levels of DObelow their survival threshold (Swales 2006). It seems likely

that Australian bass make similar movements as part of aforaging strategy, which may be part of a pelagic/epibenthicstrategy. The lack of fish caught with gill nets in water below the4mgL�1 threshold may be due to fewer fish making these

movements, or reduced activity in hypoxic water (Kramer1987), either of which would reduce the likelihood of a fishencountering a gill net. Despite the evidence of some individuals

moving into areas with DO ,4mgL�1, the consistent lack offish observed in this study below 4mgL�1 suggests this thresh-old has a strong influence on a population’s distribution.

A reduced impoundment volume had a significant impact onAustralian bass during this study. A decline in the volume ofBrogo Dam combined with bottom hypoxia to reduce the

volume of available habitat by 85% in March 2009. Thiscoincided with a greatly increased CPUE and significantlydecreased fish condition. This increase in gill netting CPUEapproximates the relative decrease in the available habitat

volume, which is observed in the reasonably consistent standar-dised CPUE values. When this impoundment was almost full(May 2008), severe bottom hypoxia reduced the volume of

available habitat by only 30% and CPUE and condition showedno obvious change. This may suggest that that the bottom 30%of a dam can become temporarily unavailable with little observ-

able impact on the stocked populations of Australian bass.The main assumption of standardised sampling in this study

was equal average catchability of the population betweensampling periods. If this was met, then the CPUE values are

an accurate approximation of relative density (Hubert andFabrizio 2007). Sampling with identical multi-panel gill netswas aimed at meeting this assumption, but it must be accepted

that the growth of fish during the study and variation intemperature-dependent activity may have contributed to somevariation in the relative catch rates. Sampling at all depths with

equal effort should mean that gill netting CPUE approximatedthe relative density of fish in the entire dam. If the assumption ofequal catchability was not met and, for example, fish increased

their activity rates with increasing density (Marchand andBoisclair 1998; Biro et al. 2003a), then the standardised catchrates could overestimate the actual density of fish. Regardless ofthe exact relationship between habitat volume and density, the

observed 4-fold increase in relative density, associated with a

reduction in the available habitat volume from 100% to 15%,must be considered an indicator of a population under stress.

There was no significant increase in relative density among theother three sampling periods, but it is interesting to note thatthe highest standardised CPUE was associated with full habitat

availability (August 2008).The observed decline in fish condition when the volume of

available habitat was lowest may be due to a chronic exposure to

high population density (Fagerlund et al. 1981). The decline incondition during March 2009 coincided with 15% availablehabitat volume and presumably a much higher populationdensity than if the impoundment was full. Density-dependent

growth, therefore, may have caused this decline in condition(Refstie and Kittelsen 1976), but equally, a reduced prey supply(Vanderploeg et al. 2009), decreased water quality (Hayes et al.

1999), or decreased habitat quality may have all contributed.The lower condition at the end of winter (August 2008) probablyindicates a normal over-winter depletion of fat reserves, which is

not uncommon (Booth and Keast 1986). Conversely, the greatlyincreased condition in May 2010 (,3 months after the rapidre-filling of Brogo Dam) is probably due to the abatement of thestressors listed above and possibly a trophic upsurge, which can

occur when an impoundment refills after a protracted period ofreduced volume (Summerfelt 1999). Gonadal development,which occurs in this species between July and September (Harris

1986), can increase body mass in female Australian bass(Harris 1987). The condition values for the August samplingperiod, therefore, may actually overestimate the nutritional

condition during this period. There is also some evidence ofsexual dimorphic growth in Australian bass in wild populations(Harris 1987), although there is no evidence for sex-biased

allometry. In any case, if the sex ratio of fish remained constantbetween sampling periods, then the condition values are robustto sexual dimorphic growth (Leclercq et al. 2010).

Conclusions and management implications

Australian bass is a flexible species, shown by the variation inthe use of the foraging zones (this study) and by differences

in the dietary niche (Smith et al. 2011). There is also evidencefor flexible behaviour in other percichthyids (Crook 2004;Howell et al. 2004; Sternberg et al. 2008). Adaptive behaviour,

or ‘ecological flexibility’ (Werner and Hall 1979), is an idealbehaviour for variable environments (Dill 1983), which makesthe Percichthyidae suited to stocked recreational fisheries in thevariable impoundments of Australia. Although the behavioural

flexibility of these species may delay the onset of density-dependent effects due to stratification, habitat restrictionwas observed to affect Australian bass, meaning site-specific

information on habitat availability should still be incorporatedinto stocking regimes. Information on habitat availabilitycould aid stocking through the elucidation of relationships

between environmental variables and stocking rates (Cowx1994; Jacobson and Anderson 2007), or through the direct esti-mation of a system’s carrying capacity (Li 1999; Aprahamian

et al. 2003).Although the use of available habitat volumes in production-

based stocking models (e.g. Taylor and Suthers 2008) may be oflimited use without precise knowledge of the relationship

between habitat volume and productivity, at the very least the


availability of habitat should be incorporated into a stockingregime in a risk-management capacity (Aprahamian et al. 2003).

For example, an impoundment that is known to lose,50% of itsvolume to hypoxia should be stocked at lower densities than onewhich loses,20%, all else being equal. Likewise, an impound-

ment that experiences high variation in flows could be stocked atdensities proportional to the trade-off between the severity of a‘drought’ event and its likelihood. In fact, an ideal factor for

deciding upon the goal for a stocked fishery, i.e. to produce a fewlarge fish, or many small ones (Walters and Post 1993), could bebased on the expected environmental variability. Brogo Dam,for example, which experiences a decline in total volume to

below 50% every 2–5 years (NSW Office of Water, personalcommunication), would be best stocked as a trophy fishery, withlow densities that are unlikely to experience severe density-

dependent limitations at times of reduced habitat volume. Thecurrent study demonstrates that habitat availability should beincluded in themanagement of stocked fisheries. Quantitatively,

this would require the use of spatially explicit stocking models,but, even without these, a qualitative consideration of habitatavailability should be included in stocking management plans.

Acknowledgements

We thank Adrian Ferguson, Alex Pursche, Leo Cameron and Justin Stanger

for their assistance in the field. The comments of three reviewers greatly

improved this manuscript. This research was funded by the NSW Recrea-

tional Freshwater Fishing Trust and the Australian Research Council

(LP0776273) and conducted under UNSW Animal Care and Ethics

Approvals (07–62A). This manuscript is Sydney Institute of Marine Science

Contribution #0054.

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